One of the critical stages in contrastive learning is data augmentation, which aims to provide different views for contrastive pairs construction.
Unlike images, the augmentation of graph data has to take not only the semantic but also the graph structural information into account.
Existing GCL methods utilize feature shuffling [1]}, edge addition and removal [2]}, subgraph sampling [3]}, etc., for graph data augmentation.
The effectiveness of existing methods relies to their carefully designed data augmentation strategies [4]}.
